Complex technical systems and machines such as gas or wind turbines change their behaviour over time. Complex technical systems such as gas or wind turbines are governed by many complicated physical interrelationships that can often only be assessed by use of statistical methods since many values or parameters can only be roughly estimated. Conventional signal evaluations are not sufficient to maximize a turbine's life span and performance. By using so-called recurrent neural networks it is possible to depict the processes of a turbine and to make forecasts regarding its output. In particular, for maintenance purposes it is important to know not only what happened in the past but also how the processes of the technical system will continue in the future. By learning different situations the evaluation system can get better at independently forecasting which settings or decisions are required for a technical system such as a wind or gas turbine.
Monitoring and diagnostics for technical systems can be realized in different time scales. Up to now, simple monitoring functions can be handled by control systems of the technical system and these monitoring functions can comprise a comparison of measured values provided for instance by physical sensors to absolute limits or thresholds. Further, ratios between different measured values can be calculated compared to predetermined ratios. Moreover, it is possible to compare results of fixed formulas calculated by the control system to predetermined ranges. Complex and more accurate diagnostics are performed offline or on separate devices that only receive part of the operational data from the technical system.
Conventional control systems lack the resources to evaluate many complex models for all measured variables in real-time. Soft sensors learned from operational data can capture complex and nonlinear dependencies but need to be retrained regularly to keep their high accuracy. This retraining to account for the current system operation conditions however leads to the situation that small and continuous changes and degradations within the technical system are not detected anymore but learned as a changed system behaviour of the technical system. A conventional way to accurately detect fast as well as slow system changes in a technical system is the use of several monitoring models for one variable that are trained on different time scales. However, this again increases the computational load of the control system.
Another conventional way to accurately detect fast as well as slow system changes is the comparison of models on data of a long period such as months or years. However, accessing these old data also creates significant performance problems. Another issue is that a changed or updated system configuration or replacements of physical sensors can lead to different measurements, causing this approach to be impractical.
Accordingly, there is a need to provide a method and an apparatus for deriving reliable diagnostic data about a complex technical system which overcome the above-mentioned disadvantages and which are able to capture a complex system behaviour and accurately identify fast as well as slowly degrading components of the technical system.